Overview

Dataset statistics

Number of variables20
Number of observations56
Missing cells171
Missing cells (%)15.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory157.6 B

Variable types

Numeric18
DateTime1
Categorical1

Alerts

Onset is highly correlated with TST and 4 other fieldsHigh correlation
Offset is highly correlated with MidpointHigh correlation
TST is highly correlated with Onset and 5 other fieldsHigh correlation
WASO is highly correlated with TIB and 2 other fieldsHigh correlation
NOA is highly correlated with Onset and 6 other fieldsHigh correlation
TIB is highly correlated with Onset and 6 other fieldsHigh correlation
REMSD is highly correlated with TST and 4 other fieldsHigh correlation
LSD is highly correlated with Onset and 5 other fieldsHigh correlation
DSD is highly correlated with SWSPHigh correlation
TSDP is highly correlated with Onset and 6 other fieldsHigh correlation
AI is highly correlated with NOAHigh correlation
SWSP is highly correlated with LSD and 1 other fieldsHigh correlation
REMP is highly correlated with REMSDHigh correlation
SMI is highly correlated with WASOHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
Onset is highly correlated with TST and 4 other fieldsHigh correlation
Offset is highly correlated with DSD and 1 other fieldsHigh correlation
TST is highly correlated with Onset and 6 other fieldsHigh correlation
WASO is highly correlated with TIB and 2 other fieldsHigh correlation
NOA is highly correlated with Onset and 6 other fieldsHigh correlation
TIB is highly correlated with Onset and 7 other fieldsHigh correlation
REMSD is highly correlated with TST and 4 other fieldsHigh correlation
LSD is highly correlated with Onset and 5 other fieldsHigh correlation
DSD is highly correlated with Offset and 4 other fieldsHigh correlation
TSDP is highly correlated with Onset and 7 other fieldsHigh correlation
AI is highly correlated with NOAHigh correlation
SWSP is highly correlated with LSD and 1 other fieldsHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SMI is highly correlated with WASO and 1 other fieldsHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
Onset is highly correlated with TST and 3 other fieldsHigh correlation
Offset is highly correlated with MidpointHigh correlation
TST is highly correlated with Onset and 5 other fieldsHigh correlation
WASO is highly correlated with SMIHigh correlation
NOA is highly correlated with TST and 1 other fieldsHigh correlation
TIB is highly correlated with Onset and 3 other fieldsHigh correlation
REMSD is highly correlated with TST and 1 other fieldsHigh correlation
LSD is highly correlated with Onset and 4 other fieldsHigh correlation
TSDP is highly correlated with Onset and 3 other fieldsHigh correlation
REMP is highly correlated with REMSDHigh correlation
SMI is highly correlated with WASOHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
df_index is highly correlated with Date and 2 other fieldsHigh correlation
Onset is highly correlated with Offset and 7 other fieldsHigh correlation
Offset is highly correlated with Onset and 10 other fieldsHigh correlation
TST is highly correlated with Onset and 10 other fieldsHigh correlation
WASO is highly correlated with Offset and 4 other fieldsHigh correlation
NOA is highly correlated with Onset and 9 other fieldsHigh correlation
TIB is highly correlated with Onset and 9 other fieldsHigh correlation
REMSD is highly correlated with Offset and 10 other fieldsHigh correlation
LSD is highly correlated with Onset and 5 other fieldsHigh correlation
DSD is highly correlated with Offset and 8 other fieldsHigh correlation
Date is highly correlated with df_index and 18 other fieldsHigh correlation
TSDP is highly correlated with Onset and 9 other fieldsHigh correlation
AI is highly correlated with df_index and 3 other fieldsHigh correlation
SWSP is highly correlated with REMSD and 2 other fieldsHigh correlation
REMP is highly correlated with TST and 4 other fieldsHigh correlation
SMI is highly correlated with Offset and 4 other fieldsHigh correlation
Midpoint is highly correlated with Onset and 7 other fieldsHigh correlation
Day is highly correlated with Date and 1 other fieldsHigh correlation
IsWeekend is highly correlated with Date and 1 other fieldsHigh correlation
SleepRegularity is highly correlated with df_index and 3 other fieldsHigh correlation
Onset has 11 (19.6%) missing values Missing
Offset has 11 (19.6%) missing values Missing
TST has 11 (19.6%) missing values Missing
WASO has 11 (19.6%) missing values Missing
NOA has 11 (19.6%) missing values Missing
TIB has 11 (19.6%) missing values Missing
REMSD has 11 (19.6%) missing values Missing
LSD has 11 (19.6%) missing values Missing
DSD has 11 (19.6%) missing values Missing
TSDP has 11 (19.6%) missing values Missing
AI has 11 (19.6%) missing values Missing
SWSP has 11 (19.6%) missing values Missing
REMP has 11 (19.6%) missing values Missing
SMI has 11 (19.6%) missing values Missing
Midpoint has 11 (19.6%) missing values Missing
SleepRegularity has 6 (10.7%) missing values Missing
df_index is uniformly distributed Uniform
Day is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique
df_index has 1 (1.8%) zeros Zeros
IsWeekend has 40 (71.4%) zeros Zeros

Reproduction

Analysis started2022-11-29 10:08:06.147776
Analysis finished2022-11-29 10:08:51.554870
Duration45.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE
ZEROS

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.5
Minimum0
Maximum55
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:51.693865image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.75
Q113.75
median27.5
Q341.25
95-th percentile52.25
Maximum55
Range55
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation16.30950643
Coefficient of variation (CV)0.5930729611
Kurtosis-1.2
Mean27.5
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance266
MonotonicityNot monotonic
2022-11-29T10:08:52.309917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
1.8%
11
 
1.8%
211
 
1.8%
221
 
1.8%
231
 
1.8%
241
 
1.8%
251
 
1.8%
261
 
1.8%
271
 
1.8%
541
 
1.8%
Other values (46)46
82.1%
ValueCountFrequency (%)
01
1.8%
11
1.8%
21
1.8%
31
1.8%
41
1.8%
51
1.8%
61
1.8%
71
1.8%
81
1.8%
91
1.8%
ValueCountFrequency (%)
551
1.8%
541
1.8%
531
1.8%
521
1.8%
511
1.8%
501
1.8%
491
1.8%
481
1.8%
471
1.8%
461
1.8%

Onset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)97.8%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean23.99296296
Minimum22.41666667
Maximum27.06666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:52.501185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum22.41666667
5-th percentile22.55333333
Q123.2
median23.75
Q324.36666667
95-th percentile26.34333333
Maximum27.06666667
Range4.65
Interquartile range (IQR)1.166666667

Descriptive statistics

Standard deviation1.167242763
Coefficient of variation (CV)0.0486493796
Kurtosis0.3514710943
Mean23.99296296
Median Absolute Deviation (MAD)0.5666666667
Skewness0.974980645
Sum1079.683333
Variance1.362455668
MonotonicityNot monotonic
2022-11-29T10:08:52.689375image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23.752
 
3.6%
23.183333331
 
1.8%
23.351
 
1.8%
23.433333331
 
1.8%
261
 
1.8%
24.366666671
 
1.8%
24.051
 
1.8%
26.11
 
1.8%
26.116666671
 
1.8%
23.783333331
 
1.8%
Other values (34)34
60.7%
(Missing)11
 
19.6%
ValueCountFrequency (%)
22.416666671
1.8%
22.433333331
1.8%
22.551
1.8%
22.566666671
1.8%
22.716666671
1.8%
22.751
1.8%
22.766666671
1.8%
22.783333331
1.8%
23.083333331
1.8%
23.151
1.8%
ValueCountFrequency (%)
27.066666671
1.8%
26.433333331
1.8%
26.41
1.8%
26.116666671
1.8%
26.11
1.8%
261
1.8%
25.383333331
1.8%
25.21
1.8%
24.91
1.8%
24.583333331
1.8%

Offset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)82.2%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean7.605185185
Minimum5.65
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:52.869389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5.65
5-th percentile6.746666667
Q17.216666667
median7.433333333
Q37.866666667
95-th percentile9.133333333
Maximum10
Range4.35
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.7744729572
Coefficient of variation (CV)0.1018348585
Kurtosis2.527483445
Mean7.605185185
Median Absolute Deviation (MAD)0.3
Skewness1.041446817
Sum342.2333333
Variance0.5998083614
MonotonicityNot monotonic
2022-11-29T10:08:53.031176image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
7.2166666674
 
7.1%
7.5333333332
 
3.6%
7.6666666672
 
3.6%
7.7333333332
 
3.6%
7.2833333332
 
3.6%
7.1333333332
 
3.6%
9.651
 
1.8%
7.51
 
1.8%
7.31
 
1.8%
7.11
 
1.8%
Other values (27)27
48.2%
(Missing)11
19.6%
ValueCountFrequency (%)
5.651
 
1.8%
6.5833333331
 
1.8%
6.7166666671
 
1.8%
6.8666666671
 
1.8%
7.0333333331
 
1.8%
7.11
 
1.8%
7.1166666671
 
1.8%
7.1333333332
3.6%
7.151
 
1.8%
7.2166666674
7.1%
ValueCountFrequency (%)
101
1.8%
9.651
1.8%
9.1833333331
1.8%
8.9333333331
1.8%
8.751
1.8%
8.41
1.8%
8.2833333331
1.8%
8.1833333331
1.8%
7.951
1.8%
7.9333333331
1.8%

TST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)86.7%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean390.9111111
Minimum230
Maximum523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:53.206439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile275.2
Q1353
median396
Q3428
95-th percentile499.2
Maximum523
Range293
Interquartile range (IQR)75

Descriptive statistics

Standard deviation66.30981767
Coefficient of variation (CV)0.1696288895
Kurtosis0.04552808424
Mean390.9111111
Median Absolute Deviation (MAD)36
Skewness-0.2975284403
Sum17591
Variance4396.991919
MonotonicityNot monotonic
2022-11-29T10:08:53.361453image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4263
 
5.4%
3522
 
3.6%
3452
 
3.6%
4802
 
3.6%
4512
 
3.6%
4471
 
1.8%
4101
 
1.8%
3751
 
1.8%
2901
 
1.8%
3601
 
1.8%
Other values (29)29
51.8%
(Missing)11
 
19.6%
ValueCountFrequency (%)
2301
1.8%
2671
1.8%
2741
1.8%
2801
1.8%
2901
1.8%
2951
1.8%
3041
1.8%
3452
3.6%
3522
3.6%
3531
1.8%
ValueCountFrequency (%)
5231
1.8%
5181
1.8%
5041
1.8%
4802
3.6%
4512
3.6%
4471
1.8%
4431
1.8%
4371
1.8%
4301
1.8%
4281
1.8%

WASO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)73.3%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean65.75555556
Minimum27
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:53.523451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile37.4
Q154
median60
Q373
95-th percentile96.2
Maximum170
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation25.86956404
Coefficient of variation (CV)0.3934202033
Kurtosis7.188363727
Mean65.75555556
Median Absolute Deviation (MAD)10
Skewness2.277408268
Sum2959
Variance669.2343434
MonotonicityNot monotonic
2022-11-29T10:08:53.682453image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
643
 
5.4%
483
 
5.4%
573
 
5.4%
542
 
3.6%
732
 
3.6%
552
 
3.6%
582
 
3.6%
662
 
3.6%
612
 
3.6%
971
 
1.8%
Other values (23)23
41.1%
(Missing)11
19.6%
ValueCountFrequency (%)
271
 
1.8%
321
 
1.8%
371
 
1.8%
391
 
1.8%
441
 
1.8%
483
5.4%
491
 
1.8%
501
 
1.8%
521
 
1.8%
542
3.6%
ValueCountFrequency (%)
1701
1.8%
1511
1.8%
971
1.8%
931
1.8%
911
1.8%
901
1.8%
821
1.8%
811
1.8%
801
1.8%
761
1.8%

NOA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)40.0%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean27.71111111
Minimum16
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:53.842907image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q124
median28
Q332
95-th percentile35.8
Maximum40
Range24
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.699370867
Coefficient of variation (CV)0.2056709615
Kurtosis-0.4101591366
Mean27.71111111
Median Absolute Deviation (MAD)4
Skewness-0.2308957121
Sum1247
Variance32.48282828
MonotonicityNot monotonic
2022-11-29T10:08:53.981871image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
335
8.9%
275
8.9%
284
 
7.1%
254
 
7.1%
324
 
7.1%
293
 
5.4%
243
 
5.4%
183
 
5.4%
202
 
3.6%
232
 
3.6%
Other values (8)10
17.9%
(Missing)11
19.6%
ValueCountFrequency (%)
161
 
1.8%
171
 
1.8%
183
5.4%
202
 
3.6%
232
 
3.6%
243
5.4%
254
7.1%
275
8.9%
284
7.1%
293
5.4%
ValueCountFrequency (%)
401
 
1.8%
371
 
1.8%
361
 
1.8%
352
 
3.6%
335
8.9%
324
7.1%
312
 
3.6%
301
 
1.8%
293
5.4%
284
7.1%

TIB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)88.9%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean456.6666667
Minimum267
Maximum613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:54.141871image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum267
5-th percentile307.8
Q1424
median462
Q3508
95-th percentile573.2
Maximum613
Range346
Interquartile range (IQR)84

Descriptive statistics

Standard deviation76.74307786
Coefficient of variation (CV)0.1680505355
Kurtosis0.2273738295
Mean456.6666667
Median Absolute Deviation (MAD)41
Skewness-0.5076898645
Sum20550
Variance5889.5
MonotonicityNot monotonic
2022-11-29T10:08:54.324213image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5282
 
3.6%
5232
 
3.6%
5032
 
3.6%
4432
 
3.6%
4722
 
3.6%
5011
 
1.8%
4891
 
1.8%
3561
 
1.8%
4211
 
1.8%
4251
 
1.8%
Other values (30)30
53.6%
(Missing)11
 
19.6%
ValueCountFrequency (%)
2671
1.8%
3061
1.8%
3071
1.8%
3111
1.8%
3431
1.8%
3461
1.8%
3561
1.8%
4001
1.8%
4031
1.8%
4211
1.8%
ValueCountFrequency (%)
6131
1.8%
5911
1.8%
5801
1.8%
5461
1.8%
5282
3.6%
5231
1.8%
5232
3.6%
5211
1.8%
5151
1.8%
5081
1.8%

REMSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)84.4%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean73.62222222
Minimum22
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:54.521203image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile42.2
Q160
median74
Q384
95-th percentile113
Maximum125
Range103
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.49675622
Coefficient of variation (CV)0.3055701871
Kurtosis-0.02674554421
Mean73.62222222
Median Absolute Deviation (MAD)13
Skewness0.1786676063
Sum3313
Variance506.1040404
MonotonicityNot monotonic
2022-11-29T10:08:54.700229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
843
 
5.4%
743
 
5.4%
802
 
3.6%
832
 
3.6%
632
 
3.6%
901
 
1.8%
441
 
1.8%
1141
 
1.8%
1091
 
1.8%
1251
 
1.8%
Other values (28)28
50.0%
(Missing)11
 
19.6%
ValueCountFrequency (%)
221
1.8%
361
1.8%
421
1.8%
431
1.8%
441
1.8%
471
1.8%
501
1.8%
521
1.8%
531
1.8%
541
1.8%
ValueCountFrequency (%)
1251
1.8%
1201
1.8%
1141
1.8%
1091
1.8%
1041
1.8%
1021
1.8%
981
1.8%
941
1.8%
901
1.8%
881
1.8%

LSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)97.8%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean234.1555556
Minimum127
Maximum338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:54.880185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum127
5-th percentile150.6
Q1212
median232
Q3263
95-th percentile309.8
Maximum338
Range211
Interquartile range (IQR)51

Descriptive statistics

Standard deviation46.49729003
Coefficient of variation (CV)0.1985743619
Kurtosis0.0339983408
Mean234.1555556
Median Absolute Deviation (MAD)30
Skewness-0.166314663
Sum10537
Variance2161.99798
MonotonicityNot monotonic
2022-11-29T10:08:55.051859image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2632
 
3.6%
2771
 
1.8%
2711
 
1.8%
2161
 
1.8%
2601
 
1.8%
1801
 
1.8%
2171
 
1.8%
2181
 
1.8%
1691
 
1.8%
1991
 
1.8%
Other values (34)34
60.7%
(Missing)11
 
19.6%
ValueCountFrequency (%)
1271
1.8%
1471
1.8%
1481
1.8%
1611
1.8%
1661
1.8%
1691
1.8%
1801
1.8%
1981
1.8%
1991
1.8%
2031
1.8%
ValueCountFrequency (%)
3381
1.8%
3231
1.8%
3131
1.8%
2971
1.8%
2831
1.8%
2821
1.8%
2771
1.8%
2741
1.8%
2711
1.8%
2661
1.8%

DSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)77.8%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean83.13333333
Minimum54
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:55.214985image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile56.2
Q168
median83
Q390
95-th percentile114.8
Maximum144
Range90
Interquartile range (IQR)22

Descriptive statistics

Standard deviation20.12303068
Coefficient of variation (CV)0.2420573057
Kurtosis1.713200846
Mean83.13333333
Median Absolute Deviation (MAD)13
Skewness1.071032712
Sum3741
Variance404.9363636
MonotonicityNot monotonic
2022-11-29T10:08:55.367742image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
903
 
5.4%
723
 
5.4%
872
 
3.6%
682
 
3.6%
562
 
3.6%
862
 
3.6%
832
 
3.6%
1022
 
3.6%
1001
 
1.8%
541
 
1.8%
Other values (25)25
44.6%
(Missing)11
19.6%
ValueCountFrequency (%)
541
1.8%
562
3.6%
571
1.8%
581
1.8%
601
1.8%
631
1.8%
651
1.8%
661
1.8%
671
1.8%
682
3.6%
ValueCountFrequency (%)
1441
 
1.8%
1411
 
1.8%
1161
 
1.8%
1101
 
1.8%
1081
 
1.8%
1022
3.6%
1001
 
1.8%
991
 
1.8%
971
 
1.8%
903
5.4%

Date
Date

HIGH CORRELATION
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size576.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-25 00:00:00
2022-11-29T10:08:55.540771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:08:55.735840image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TSDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)93.3%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean456.7333333
Minimum267
Maximum613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:55.906931image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum267
5-th percentile308
Q1424
median462
Q3508
95-th percentile573.2
Maximum613
Range346
Interquartile range (IQR)84

Descriptive statistics

Standard deviation76.71861218
Coefficient of variation (CV)0.1679724395
Kurtosis0.2253888253
Mean456.7333333
Median Absolute Deviation (MAD)41
Skewness-0.506865304
Sum20553
Variance5885.745455
MonotonicityNot monotonic
2022-11-29T10:08:56.075603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5282
 
3.6%
5032
 
3.6%
4432
 
3.6%
5011
 
1.8%
4721
 
1.8%
3561
 
1.8%
4211
 
1.8%
4251
 
1.8%
3461
 
1.8%
4241
 
1.8%
Other values (32)32
57.1%
(Missing)11
 
19.6%
ValueCountFrequency (%)
2671
1.8%
3061
1.8%
3071
1.8%
3121
1.8%
3431
1.8%
3461
1.8%
3561
1.8%
4001
1.8%
4031
1.8%
4211
1.8%
ValueCountFrequency (%)
6131
1.8%
5911
1.8%
5801
1.8%
5461
1.8%
5282
3.6%
5241
1.8%
5231
1.8%
5231
1.8%
5211
1.8%
5151
1.8%

AI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)100.0%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean4.258461851
Minimum3
Maximum5.77540107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:56.258245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.426006904
Q13.941605839
median4.285714286
Q34.647887324
95-th percentile5.153234494
Maximum5.77540107
Range2.77540107
Interquartile range (IQR)0.7062814845

Descriptive statistics

Standard deviation0.5598159029
Coefficient of variation (CV)0.1314596496
Kurtosis0.2600177132
Mean4.258461851
Median Absolute Deviation (MAD)0.3579490487
Skewness0.1362556062
Sum191.6307833
Variance0.3133938451
MonotonicityNot monotonic
2022-11-29T10:08:56.422209image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
4.4295302011
 
1.8%
4.3902439021
 
1.8%
4.6956521741
 
1.8%
4.5145631071
 
1.8%
4.8813559321
 
1.8%
4.5040214481
 
1.8%
3.841
 
1.8%
3.5172413791
 
1.8%
31
 
1.8%
4.0152963671
 
1.8%
Other values (35)35
62.5%
(Missing)11
 
19.6%
ValueCountFrequency (%)
31
1.8%
3.3590733591
1.8%
3.4090909091
1.8%
3.4936708861
1.8%
3.51
1.8%
3.5046728971
1.8%
3.5172413791
1.8%
3.5920177381
1.8%
3.767441861
1.8%
3.841
1.8%
ValueCountFrequency (%)
5.775401071
1.8%
5.2459016391
1.8%
5.1980198021
1.8%
4.9740932641
1.8%
4.8813559321
1.8%
4.7826086961
1.8%
4.7619047621
1.8%
4.7368421051
1.8%
4.6969696971
1.8%
4.6956521741
1.8%

SWSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)100.0%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean21.48161336
Minimum12.81464531
Maximum29.13043478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:56.601224image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12.81464531
5-th percentile13.9866727
Q118.40354767
median21.44638404
Q325
95-th percentile27.63135165
Maximum29.13043478
Range16.31578947
Interquartile range (IQR)6.596452328

Descriptive statistics

Standard deviation4.323811241
Coefficient of variation (CV)0.2012796324
Kurtosis-0.8539415358
Mean21.48161336
Median Absolute Deviation (MAD)3.215821037
Skewness-0.250377477
Sum966.6726012
Variance18.69534365
MonotonicityNot monotonic
2022-11-29T10:08:56.804749image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20.134228191
 
1.8%
13.902439021
 
1.8%
24.637681161
 
1.8%
17.475728161
 
1.8%
24.406779661
 
1.8%
18.2305631
 
1.8%
241
 
1.8%
25.517241381
 
1.8%
251
 
1.8%
26.959847041
 
1.8%
Other values (35)35
62.5%
(Missing)11
 
19.6%
ValueCountFrequency (%)
12.814645311
1.8%
13.615023471
1.8%
13.902439021
1.8%
14.323607431
1.8%
15.151515151
1.8%
15.258215961
1.8%
16.666666671
1.8%
16.8751
1.8%
17.475728161
1.8%
18.2305631
1.8%
ValueCountFrequency (%)
29.130434781
1.8%
28.32861191
1.8%
27.79922781
1.8%
26.959847041
1.8%
26.829268291
1.8%
26.185101581
1.8%
26.066350711
1.8%
25.822784811
1.8%
25.517241381
1.8%
25.468164791
1.8%

REMP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)100.0%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean18.69433011
Minimum6.232294618
Maximum28.21782178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:56.988067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.232294618
5-th percentile12.97776766
Q115.70048309
median18.18181818
Q321.02272727
95-th percentile25.54613622
Maximum28.21782178
Range21.98552716
Interquartile range (IQR)5.322244181

Descriptive statistics

Standard deviation4.241046402
Coefficient of variation (CV)0.2268627106
Kurtosis0.8086537908
Mean18.69433011
Median Absolute Deviation (MAD)2.777777778
Skewness-0.05732888367
Sum841.2448551
Variance17.98647458
MonotonicityNot monotonic
2022-11-29T10:08:57.155701image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
17.897091721
 
1.8%
17.073170731
 
1.8%
12.753623191
 
1.8%
19.417475731
 
1.8%
14.576271191
 
1.8%
23.59249331
 
1.8%
17.866666671
 
1.8%
16.206896551
 
1.8%
19.722222221
 
1.8%
22.944550671
 
1.8%
Other values (35)35
62.5%
(Missing)11
 
19.6%
ValueCountFrequency (%)
6.2322946181
1.8%
12.708333331
1.8%
12.753623191
1.8%
13.874345551
1.8%
14.204545451
1.8%
14.576271191
1.8%
14.754098361
1.8%
14.973262031
1.8%
15.071770331
1.8%
15.328467151
1.8%
ValueCountFrequency (%)
28.217821781
1.8%
27.631578951
1.8%
25.829383891
1.8%
24.413145541
1.8%
24.131274131
1.8%
23.59249331
1.8%
23.02483071
1.8%
23.004694841
1.8%
22.944550671
1.8%
22.51
1.8%

SMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)100.0%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean85.71808508
Minimum67.36641221
Maximum91.20521173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:57.330508image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum67.36641221
5-th percentile81.07936337
Q184.90566038
median86.89655172
Q388
95-th percentile89.47829831
Maximum91.20521173
Range23.83879951
Interquartile range (IQR)3.094339623

Descriptive statistics

Standard deviation4.440408373
Coefficient of variation (CV)0.05180246816
Kurtosis8.773646737
Mean85.71808508
Median Absolute Deviation (MAD)1.578444057
Skewness-2.650134772
Sum3857.313829
Variance19.71722652
MonotonicityNot monotonic
2022-11-29T10:08:57.502439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
89.221556891
 
1.8%
86.864406781
 
1.8%
80.985915491
 
1.8%
87.473460721
 
1.8%
82.865168541
 
1.8%
88.598574821
 
1.8%
88.235294121
 
1.8%
83.81502891
 
1.8%
84.905660381
 
1.8%
85.318107671
 
1.8%
Other values (35)35
62.5%
(Missing)11
 
19.6%
ValueCountFrequency (%)
67.366412211
1.8%
69.980119281
1.8%
80.985915491
1.8%
81.453154881
1.8%
81.696428571
1.8%
82.149712091
1.8%
82.765151521
1.8%
82.865168541
1.8%
83.81502891
1.8%
84.424379231
1.8%
ValueCountFrequency (%)
91.205211731
1.8%
90.909090911
1.8%
89.542483661
1.8%
89.221556891
1.8%
89.183222961
1.8%
89.029535861
1.8%
88.779527561
1.8%
88.629737611
1.8%
88.598574821
1.8%
88.565488571
1.8%

Midpoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)95.6%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean4.245740741
Minimum3.358333333
Maximum5.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:57.670489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.358333333
5-th percentile3.611666667
Q13.891666667
median4.175
Q34.425
95-th percentile5.093333333
Maximum5.65
Range2.291666667
Interquartile range (IQR)0.5333333333

Descriptive statistics

Standard deviation0.534505761
Coefficient of variation (CV)0.1258922279
Kurtosis0.4310215343
Mean4.245740741
Median Absolute Deviation (MAD)0.2833333333
Skewness0.8585213802
Sum191.0583333
Variance0.2856964085
MonotonicityNot monotonic
2022-11-29T10:08:57.837203image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.42
 
3.6%
3.6916666672
 
3.6%
4.1166666671
 
1.8%
3.9251
 
1.8%
4.9666666671
 
1.8%
3.8751
 
1.8%
3.5916666671
 
1.8%
4.9833333331
 
1.8%
5.651
 
1.8%
5.1083333331
 
1.8%
Other values (33)33
58.9%
(Missing)11
 
19.6%
ValueCountFrequency (%)
3.3583333331
1.8%
3.4251
1.8%
3.5916666671
1.8%
3.6916666672
3.6%
3.7166666671
1.8%
3.7333333331
1.8%
3.7751
1.8%
3.7751
1.8%
3.851
1.8%
3.8751
1.8%
ValueCountFrequency (%)
5.651
1.8%
5.6251
1.8%
5.1083333331
1.8%
5.0333333331
1.8%
4.9833333331
1.8%
4.9666666671
1.8%
4.951
1.8%
4.9251
1.8%
4.8333333331
1.8%
4.5751
1.8%

Day
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size540.0 B
Monday
Tuesday
Wednesday
Thursday
Friday
Other values (2)
16 

Length

Max length9
Median length7
Mean length7.142857143
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSunday
3rd rowMonday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday8
14.3%
Tuesday8
14.3%
Wednesday8
14.3%
Thursday8
14.3%
Friday8
14.3%
Saturday8
14.3%
Sunday8
14.3%

Length

2022-11-29T10:08:57.991251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-29T10:08:58.099251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
monday8
14.3%
tuesday8
14.3%
wednesday8
14.3%
thursday8
14.3%
friday8
14.3%
saturday8
14.3%
sunday8
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IsWeekend
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2857142857
Minimum0
Maximum1
Zeros40
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size352.0 B
2022-11-29T10:08:58.203215image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4558423058
Coefficient of variation (CV)1.59544807
Kurtosis-1.089622642
Mean0.2857142857
Median Absolute Deviation (MAD)0
Skewness0.974996043
Sum16
Variance0.2077922078
MonotonicityNot monotonic
2022-11-29T10:08:58.316215image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
040
71.4%
116
 
28.6%
ValueCountFrequency (%)
040
71.4%
116
 
28.6%
ValueCountFrequency (%)
116
 
28.6%
040
71.4%

SleepRegularity
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct48
Distinct (%)96.0%
Missing6
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.4897210343
Minimum0.1736722115
Maximum0.8719988214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:08:58.470487image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1736722115
5-th percentile0.258812273
Q10.3431000748
median0.4586926795
Q30.6533251241
95-th percentile0.7975602907
Maximum0.8719988214
Range0.6983266099
Interquartile range (IQR)0.3102250493

Descriptive statistics

Standard deviation0.1845024813
Coefficient of variation (CV)0.3767501667
Kurtosis-0.8026034073
Mean0.4897210343
Median Absolute Deviation (MAD)0.1380161999
Skewness0.3889100453
Sum24.48605171
Variance0.03404116561
MonotonicityNot monotonic
2022-11-29T10:08:58.643514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.25934836432
 
3.6%
0.39703764172
 
3.6%
0.81352570581
 
1.8%
0.4642902931
 
1.8%
0.52775793611
 
1.8%
0.48765310991
 
1.8%
0.49083971
 
1.8%
0.45010029751
 
1.8%
0.52223478121
 
1.8%
0.65752587611
 
1.8%
Other values (38)38
67.9%
(Missing)6
 
10.7%
ValueCountFrequency (%)
0.17367221151
1.8%
0.19056142691
1.8%
0.25837365281
1.8%
0.25934836432
3.6%
0.26273137951
1.8%
0.28500324881
1.8%
0.28714349881
1.8%
0.30320052041
1.8%
0.31917863341
1.8%
0.32217432591
1.8%
ValueCountFrequency (%)
0.87199882141
1.8%
0.85518435311
1.8%
0.81352570581
1.8%
0.77804700561
1.8%
0.77598290631
1.8%
0.74153400811
1.8%
0.73542495721
1.8%
0.72924772791
1.8%
0.71851229681
1.8%
0.68054502751
1.8%

Interactions

2022-11-29T10:08:47.635794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:08:09.798808image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T10:08:14.137833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

2022-11-29T10:08:58.827490image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T10:08:59.097471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T10:08:59.372495image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T10:08:59.658468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T10:08:50.093957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T10:08:50.673191image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-29T10:08:51.023485image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-29T10:08:51.368682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexOnsetOffsetTSTWASONOATIBREMSDLSDDSDDateTSDPAISWSPREMPSMIMidpointDayIsWeekendSleepRegularity
0023.1833337.533333447.054.033.0501.080.0277.090.02022-10-01501.04.42953020.13422817.89709289.2215574.175000Saturday1NaN
1122.5500007.350000437.091.032.0528.084.0297.056.02022-10-02528.04.39359312.81464519.22196882.7651524.400000Sunday1NaN
2222.7500006.866667426.061.028.0487.0104.0264.058.02022-10-03487.03.94366213.61502324.41314687.4743334.058333Monday00.173672
3323.1500006.583333382.064.028.0446.053.0247.082.02022-10-04446.04.39790621.46596913.87434685.6502243.716667Tuesday00.285003
4424.5833337.250000352.048.020.0400.074.0208.070.02022-10-05400.03.40909119.88636421.02272788.0000003.916667Wednesday00.258374
5522.4333337.116667428.093.025.0521.090.0236.0102.02022-10-06521.03.50467323.83177621.02803782.1497124.341667Thursday00.259348
645NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-07NaNNaNNaNNaNNaNNaNFriday00.259348
746NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-08NaNNaNNaNNaNNaNNaNSaturday10.287143
847NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-09NaNNaNNaNNaNNaNNaNSunday10.262731
948NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-10NaNNaNNaNNaNNaNNaNMonday00.319179

Last rows

df_indexOnsetOffsetTSTWASONOATIBREMSDLSDDSDDateTSDPAISWSPREMPSMIMidpointDayIsWeekendSleepRegularity
463524.2500007.533333377.060.027.0437.066.0257.054.02022-11-16437.04.29708214.32360717.50663186.2700233.891667Wednesday00.775983
473623.5500008.283333353.0170.025.0523.022.0231.0100.02022-11-17524.04.24929228.3286126.23229567.3664124.366667Thursday00.680545
483722.7166677.300000451.064.027.0515.082.0282.087.02022-11-18515.03.59201819.29046618.18181887.5728164.291667Friday00.660903
493826.4000007.500000274.032.018.0306.042.0166.066.02022-11-19306.03.94160624.08759115.32846789.5424844.950000Saturday10.482025
503923.8000009.650000518.073.029.0591.0125.0249.0144.02022-11-20591.03.35907327.79922824.13127487.6480544.925000Sunday10.438477
514025.3833337.100000304.039.023.0343.084.0148.072.02022-11-21343.04.53947423.68421127.63157988.6297384.241667Monday00.425540
524123.5666677.466667422.052.033.0474.0109.0203.0110.02022-11-22474.04.69194326.06635125.82938489.0295363.950000Tuesday00.422718
534223.8333337.216667386.057.032.0443.074.0224.088.02022-11-23443.04.97409322.79792719.17098487.1331833.691667Wednesday00.465347
544323.8833337.433333404.049.035.0453.0114.0212.078.02022-11-24453.05.19802019.30693128.21782289.1832233.775000Thursday00.512183
554423.4000007.266667414.058.033.0472.065.0263.086.02022-11-25472.04.78260920.77294715.70048387.7118643.933333Friday00.526287